Knowledge discovery based on brainwave response to external stimulation

ABSTRACT

Techniques are disclosed for detecting knowledge based on brainwave response to external stimulation. A subject can be exposed to stimuli and brainwave responses indicating a p-300 signal can be detected. Further stimuli or sequences of stimuli can be selected be presented to the subject based on the category correlated with the stimuli that indicate a p-300 response.

This application claims priority to U.S. Provisional Application No.61/916,331, filed Dec. 16, 2013, the disclosure of which is incorporatedby reference in its entirety.

FIELD OF THE INVENTION

The present invention relates to psychophysiological measurement and,more specifically, to techniques for detecting knowledge based onbrainwave response to external stimulation.

BACKGROUND

For decades Electroencephalography (EEG) and related tools that measurepsychophysiological responses (e.g., polygraphs) have been used todiscern whether someone is familiar with certain information. Examplesof EEG tools include the systems and methods disclosed in U.S. Pat. No.8,684,926 B2 and U.S. Patent Application publication 20140163409 A1(each of which is incorporated by reference in its entirety). Systemssuch as these, are generally used in conjunction with the GuiltyKnowledge Test (GKT). The purpose of the GKT is to associate the testsubject to a particular event (e.g., a crime) by observation andinterpretation of the test subject's psychophysiologic response whenconfronted with information that could only be known by someone familiarwith the event.

Success of the GKT requires that the investigator know about the people,places or things associated with an event in order to pose verbal ornon-verbal questions to the test subject. The investigator compares thetest subject's psychophysiologic response to questions known to berelated to the event with questions known to be unrelated to the event.The reliability of test results depends upon the test administrator'sknowledge of what the test subject knows or is believed to know andsubjective interpretation of observed psychophysiologic response of thetest subject. There is a need for a non-verbal means of deducing what aperson is familiar with by objective interpretation of psychophysiologicresponses to that do not rely upon a priori knowledge of what the testsubject knows.

SUMMARY OF THE INVENTION

In a first example, a method is disclosed for exposing a subject to afirst sequence of stimuli. A subject is exposed to a first sequence ofstimuli. At least one stimulus of the first sequence of stimulicorrelated with a category. A brainwave response of the subject to theat least one stimulus of the first sequence of stimuli is detected. Thedetected brainwave response is correlated to at least one targetcategory, and a second sequence of stimuli is selected, based upon thebrainwave response of the subject to the at least one stimulus of thefirst sequence of stimuli.

In a second example, a system is disclosed that includes one or moresensors, a presentation device, and a processor. The processor is incommunication with the presentation device and the at least one sensorand adapted and configured to send at least one stimulus in a firstsequence of stimuli to the presentation device, receive a brainwaveresponse from the at least one sensor based upon the brainwave responseof a subject, correlate the detected brainwave response to at least onetarget category, and select a second sequence of stimuli, based upon thebrainwave response of the subject to the at least one stimulus of thefirst sequence of stimuli.

For the purposes of this disclosure, a sequence of stimuli can be madeup of a single stimulus or a set of stimuli.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flow diagram for a first method for knowledge detection.

FIG. 2 shows a flow diagram for a second method for knowledge detection.

FIG. 3 shows a system diagram for a system for knowledge detection.

FIG. 4 shows a system diagram of an embodiment of this disclosurerelated to vocational knowledge discovery.

FIG. 5 shows multiple EEG time series illustrating the character andlatency of p-300 signals.

FIG. 6 shows a system diagram of an embodiment of this disclosurerelated to automated generation of stimulus sequences.

FIG. 7 shows a system diagram of an embodiment of this disclosure forvocational knowledge detection.

DETAILED DESCRIPTION

In contrast to the GKT described above, alternative techniques, such asthose disclosed herein, do not require a priori knowledge of what thetest subject knows. The knowledge of the test subject may be deducedwithout verbal questions by observing the psychophysiologic response ofthe subject to one or more sequences of stimuli (i.e. “decks”) ofinformation which may be selected based on the subject's responses tostimuli presented previously.

Research has established that a test subject's degree of familiaritywith external stimuli such as images and sounds is correlated tostrength and timing of brainwave signals observed byelectroencephalograms (EEGs). The human brain can absorb and processstimuli at very high rates of presentation. Visual stimuli can bepresented rapidly in a technique known as Rapid Serial VisualPresentation. In addition to images, alternative stimuli such as hearingstimuli, touch stimuli, smell stimuli, and taste stimuli may bepresented using an analogous rapid serial presentation technique(collectively, the rapid serial presentation of any stimuli are referredto herein as “RSP”). The RSP technique typically displays stimuli to atest subject at rates of 4 to 12 stimuli per second. Depending upon thecontent and complexity of the information presented and the ability ofthe test subject to process the information, the presentation rate maybe more or less than the typical values.

The brain processes stimuli and produces psychophysiologic response torecognition of the stimuli in the form of brainwaves observed by EEG. Aresponse pattern strongly associated with recognition is the “p-300”brainwave which has a characteristic shape observed at about 300milliseconds (ms) after being exposed to a recognized stimulus. EEG datatime-tagged with the display of stimuli presented by the RSP techniqueenables a subject to be exposed to a large number of stimuli on aparticular topic in a relatively short period of time. For instance,assuming a presentation pattern of 3 seconds of display followed by 3seconds of rest, and a display rate of 8 images per second, a subjectcould be exposed to 240 images per minute.

EEG can be quantified in various ways by applying a Fouriertransformation, including by amplitude, power, frequency, and in orderto generate numerical values, ratios, or percentages; graphicallydisplay arrays or trends; and set thresholds. Many quantitative EEGmeasures can be used to quantify slowing or attenuation of fasterfrequencies in the EEG. These include the calculation of power withindifferent frequency bands (i.e., delta, theta, alpha, and beta); ratiosor percentages of power in specific frequency bands; and spectral edgefrequencies (based on the frequency under which x % of the EEG resides).These discrete values can then be compared between different regions,such as hemispheres, or between electrode-pair channels. Time-compressedspectral arrays (“Spectrograms”) incorporate both power and frequencyspectrum data, and can be represented using color to show power atdifferent frequencies. Additional measures include amplitude integratedEEG, which continuously monitors comatose patients by average ranges ofpeak-to-peak amplitudes displayed using a logarithmic scale, and thecommercial Bispectral Index. Other nonparametric methods exist beyondFourier transformation, including interval or period analysis andalternative transformation techniques. Parametric, mimetic, andspatiotemporal analyses are also available using a variety ofcomputational methods and waveform analysis based on machine learningapproaches trained on EEG recordings. Basic measures of total power canbe quantified and compared to performance characteristics to identifycorrelations that can be used to predict the reoccurrence of thoseperformance characteristics.

Signal processing can discriminate between brainwave signals indicatingrecognition or familiarity with of the stimulus presented (e.g., imagesor sounds) and non-recognition or unfamiliarity with the stimuli. Thecharacter and latency of the p-300 varies by individual, the sensestimulated (e.g., visual, auditory), and with time for any particularindividual. FIG. 5 illustrates an EEG time series of five visual targettrials from a representative subject, and depicts the trial-to-trialvariability of amplitudes and latencies for the p-300 component, Wangand Ding, Clinical Neurophysiology, Volume 122, Issue 5, May 2011, Pages916-924.

Brainwave response to recognition also has repeatable and predictablecharacteristics which can be exploited by digital signal processingalgorithms. The brainwave discriminator, often referred to as theclassifier component, can be trained in the characteristic nature of thetest subject's EEG response when presented with stimulus records ortargets known to be familiar to the test subject. The response to suchtarget records provides the classifier component with exemplarcharacteristics to discriminate records that are not known to be knownby the test subject but probe what the test subject recognizes.Alternatively, the classifier component can learn the difference betweenrecognition and non-recognition brainwave response by observing thebrainwave response to a deck containing stimuli that are not necessarilyknown to be known by the test subject but are likely to be recognized.Examples might include images of famous persons or an image of the testsubject.

Depending upon the individual test subject and the type of stimulipresented, brainwave indications of recognition in the p-300 may vary inamplitude, character, and latency. A brainwave classifier componentalgorithm may correlate indications of recognition in brainwaves otherthan the typical p-300 to strengthen the confidence in recognition ornon-recognition.

A test subject may intentionally or unintentionally create circumstancesthat adversely affect EEG data such that EEG recognition signals aresuppressed, masked, or otherwise corrupted. A test-subject that becomesinattentive or intentionally suppresses the senses targeted by thestimuli (e.g., for visual stimuli, averting eyes from display) will notproduce responses indicating recognition. Brainwave indications ofinattentiveness and external indications of suppressed senses can beused to flag the recognition scoring algorithm to disregard those tests.When the test subject is again attentive to the stimuli, the recognitionscores will again be useful indicators of recognition.

Likewise, intentional or unintentional masking of brainwave signals canbe accomplished by muscle movements in the face and scalp. EEG signalsassociated with muscle movement is typically much larger than EEGsignals resulting from brain functions. Signals resulting from eyeblinks, jaw clinching or scalp motion can be automatically discriminatedfrom brainwave signals and therefore used to adjust recognition scoresfor target and non-target stimuli.

Presentation of a particular deck may be repeated more than once tostrengthen statistical confidence in the EEG indications of recognitionor familiarity with particular stimuli. Shuffling the deck (i.e.,reordering the target and non-target stimuli) each time it is presentedensures that the brainwave signals observed for target stimuli are dueto the content of the stimulus rather than the presentation order.

The general features of this disclosure provide for an automated systemthat characterizes brainwave signals from the EEG data to indicate thelevel of recognition of stimuli presented in multiple sequences ofstimuli that are presented to the test subject. The system may haveaccess to category repositories of target stimuli and non-targetstimuli. Target stimuli may represent information, which would bevaluable to know that the subject possesses. Non-target stimulirepresent information, which generally would not be thought to bevaluable to know that the subject possesses. Automated indication ofrecognition of target stimuli in one deck may guide automated selectionof target stimuli in subsequent decks to obtain additional detail of thesubject's knowledge, interest, and experience. Within the broad targetand non-target categories are further categories of stimuli, which maybe classified according to the topics or subject matter to which theyare related. Decks may be first presented to the test subject withstimuli that cover broad subject areas covering major divisions of atopic. Depending upon which stimuli records result in brainwaveindications of recognition, subsequent decks with stimuli coveringsimilar or related topics in greater detail and specificity may beselected and presented to the test subject to discover additionalknowledge, interest, and experience

FIG. 1 shows a flow diagram of an embodiment of this disclosure for amethod 100 to detect knowledge of a subject by exposing the subject tostimuli and correlating certain detected brainwave responses tocategories correlated with the stimuli presented. The subject is exposedto the first sequence of stimuli 105. Each stimulus of the firstsequence of stimuli is correlated with a category. A brainwave responseof the subject to each stimulus of the first sequence of stimuli isdetected 110, and, based on the occurrence of a p-300 signal, thedetected brainwave response is correlated 115 to at least one targetcategory. In an embodiment the method may further comprise selecting asecond sequence of stimuli 120, based upon the brainwave response of thesubject to each stimulus of the first sequence of stimuli.

FIG. 2 shows a flow diagram of another embodiment of this disclosure fora method 200 to detect knowledge of a subject by exposing the subject tostimuli and correlating certain detected brainwave responses tocategories correlated with the stimuli presented. At least one stimulusof the first sequence of stimuli is correlated with a category. In someembodiments there may be a large number of categories, such as thoserelated to the lower levels of abstraction in the vocational knowledgediscovery embodiment discussed below. The subject is exposed to a firststimulus of the first sequence of stimuli 205. A brainwave response isdetected 210, and, based on the occurrence of a p-300 signal, thedetected brainwave response is correlated 215 to at least one targetcategory. A second stimulus of the first sequence of stimuli may beselected 220 and exposed 225 to the subject based upon a brainwaveresponse to at least one prior stimulus of the first sequence ofstimuli.

FIG. 3 shows a system diagram of another embodiment of this disclosurefor a system 300 to detect knowledge of a subject by exposing thesubject to stimuli and correlating certain detected brainwave responsesto categories correlated with the stimuli presented. This embodimentincludes one or more sensors 305. These sensors may be electrodes or anyother component suitable for detecting EEG signals. The electrodes maybe individually wired or part of a connected array. The sensors may beany that are suitable to take a reading from a human subject 310.Typically, but not necessarily, the sensors may be placed on the scalpwith a conductive gel or paste. Caps or netted devices may also be used.A presentation device 315 is included, such as an audio video system,computer, or similar device capable of generating stimuli that may beexperienced by a subject. The presentation device may also be any devicesuitable for generating smell stimuli, touch stimuli, or taste stimuli.A processor 320 is also included for executing instructions to exposethe subject to a first stimulus of a first sequence of stimuli 325through the presentation device. At least one stimulus of the firstsequence of stimuli are correlated with a category. It should beunderstood that non-target stimuli may be similar to target stimuli fora particular subject matter category but not necessarily representativeof that category. The processor detects 330 a brainwave response of thesubject to each stimulus of the first sequence of stimuli and, based onthe occurrence of a p-300 signal, the detected brainwave response iscorrelated 335 to at least one target category. A second stimulus of thefirst sequence of stimuli, may be selected 340 based upon a brainwaveresponse to at least one prior stimulus of the first sequence ofstimuli. And the subject may be exposed 345 to the second stimulus ofthe first sequence of stimuli.

More generally, a subject such as a person may be exposed to a firstsequence of stimuli. The person may be exposed to the stimuli at a rateof at least 3 stimuli per second, although significantly slower ratesare also contemplated herein. The first sequence can have one or morestimuli. A stimulus can be a sight stimulus, a sound stimulus, a touchstimulus, a smell stimulus, and a taste stimulus or any combinationthereof. One or a more than one of the stimuli in the first sequence maybe associated with a category. For example, a single stimulus such as aphotograph of a football game, may be associated with a category such asa “football.” The category may be an occupational category, such as“football player” or “referee.” Likewise, a group of stimuli may beassociated with a category. For example, a photograph showing a footballgame, a photograph showing a bicycle race and a photograph showing atennis match may be associated as a group with a category, such as“sports.” The category may be an occupational category, such as“athlete.”

A brainwave response of the subject to stimuli can be detected usingsensors. The response may be a p-300 signal. The response can becorrelated to at least one target category. A second sequence of stimulimay be selected based upon the brainwave response to one or more stimuliin the first sequence. The second stimuli may be selected automaticallyor by a user. The first sequence of stimuli can be a baseline sequence.

Proper assembly of the sequence of a deck is a key contributor of thecertain embodiments of this disclosure. A test deck may be composed of(i) a small number of target stimuli used to probe the test subject'sfamiliarity on a topic or range of topics and (ii) a larger number ofnon-target items unlikely to be recognized by the test subject butsimilar in gross characteristics of the target items. For example, theratio of target to non-target items may range between 1:25 to 1:2. Whenthe targets are used to isolate topics of familiarity in a deck thatcovers a broad range of topics the ratio of targets to non-targets canbe larger because many more of the intended probing target stimuli willalso be unfamiliar to the test subject. The size of decks at aparticular level of abstraction can be small or large. The deck may bebroken into subsets or hands to accommodate the attention span of thetest subject or allow more frequent periods of rest between hands.

As stated above, comparison of the brainwave response for target andnon-target stimuli provides insight into the test subject's knowledge.In an embodiment where the stimuli are images, target and non-targetimages in the deck may be selected from images of people, places,things, numbers, letters, words, and symbols. Target and non-targetimages in the deck are selected to be similar in physical attributessuch as size, color, resolution, and composition. In an embodiment wherethe stimuli are sound stimuli, examples may include audio clips, voice,music, and the sounds that relevant things make. Similar to visualpresentation decks, sound decks are more diagnostic if target andnon-target clips are similar in attributes such as volume level andbackground noise levels. This minimizes the occurrencepsychophysiological responses that can be more strongly associated withsurprise or startle than with the desired response of recognition.

Decks designed to explore the depth and breadth of familiarity of aparticular category can be compiled beforehand or created on the fly byan automated system that employs machine learning techniques to populatenew decks of stimuli based on indications of familiarity observed inprevious decks of stimuli. FIG. 6 depicts one embodiment wherein newdecks may be generated to validate what was indicated as familiar inearlier decks and introduce new stimuli that probe a deeper level ofknowledge on topics of familiarity. FIG. 6 shows a system diagram 600,where target stimuli 605 and non-target stimuli 610 may be characterizedby characterization process 615 and made accessible to search engine620. Stimulus deck generator 640 draws a sequence of stimuli from thesearch engine and sends the sequence to the randomizer 645. Optionally,a user may access the sequence of stimuli through the investigatorinterface 630 and edit the sequence through the editor 635. The stimuliare then presented 650 to the subject 655, and the subject's EEG signalsare collected 660, analog processed 665, converted 670, and digitallyprocessed 675. The p-300 signal may be detected 680, characterized 685,scored 690, and entered into a hierarchy of scores 625 for targetstimuli. The stimulus deck generator can then draw on the previoustarget hierarchy to generate more relevant decks for furtherpresentation to the subject. For Example, the stimulus deck generatormay select target stimuli of the same categories as those that are nearthe top of the target stimuli hierarchy. This process may also beperformed after each stimulus is presented, such that the deck generatormay continuously improve the relevancy of the target stimulus to bepresented within a given deck.

Alternatively, it may be advantageous for the deck to be manuallycompiled by a user (e.g., an investigator). FIG. 4 depicts one suchembodiment. In this system diagram embodiment 400, a user may compile astimulus deck by accessing, through investigator interface 420, thestimulus deck library 415 that draws on target stimuli 405 andnon-target stimuli 410. The investigator may compile and edit the deckvia editor 425 and then enter the deck into randomizer 430. The stimuliare then presented 435 to the subject 440, and the subject's EEG signalsare collected 445, analog processed 450, converted 455, and digitallyprocessed 460. The p-300 signal may be detected 465, characterized 470,and scored 475. The user then has access to the scoring through theinvestigator interface.

As shown in FIG. 7 and discussed below, another embodiment of thisdisclosure is described in relation to vocational knowledge detection700. The purpose of this example is to identify the profession of a testsubject.

Multiple decks of stimuli are presented to the test subject at variouslevels of abstraction or detail in order to guide selection of topics oflater decks with increased level of detail, and which further narrow thescope of the search. The lower level decks 720 have increased resolutionand specificity of characteristics or knowledge unique to specificprofessions. In this example, the first level of abstraction is todetermine if the test subject operates in a quantitative 710 ornon-quantitative 715 division of professions—a very high level ofabstraction. From there, decks are presented to the test subject todetermine which category of profession (e.g. 720) within thequantitative or non-quantitative division the test subject operates.Examples of professional disciplines are engineering, medicine or thearts. Once the category is determined, decks of stimuli for specificprofessions (e.g. 725) such as mechanical engineer, graphical artist orcriminal lawyer are present to establish the specific profession of thetest subject. This simple three-tiered example (i.e., division,discipline, and specific profession) is not intended to describe thefull breadth and depth of potential vocational applications.

At the highest level of abstraction, the division level, test decks arecompiled to establish quantitative or non-quantitative division ofprofessions. Example content of decks of target stimuli might include:names of principal concepts or persons key to specific professionaldisciplines, technical terms unique with quantitative andnon-quantitative disciplines, fundamental equations used in quantitativeprofessional disciplines, mathematical constants used in thequantitative professions, symbols commonly used in quantitative andnon-quantitative disciplines, or acronyms commonly used in quantitativeand non-quantitative disciplines. If the dealer is a user, she can be inclose proximity with the test subject as the decks are presented.Alternatively, the dealer may be located remotely and monitor events byelectronic communications.

This approach to vocational evaluation does not rely on binary decisionof familiar or not familiar with a particular person, place, or thing asin the GKT. Instead, this technique uses multiple steps with increasinglevel of detail to discover the profession of a test subject withoutknowing anything about the test subject beforehand. The progression toeach lower level of detail is guided by the positive response offamiliarity with one or more targets in the test deck at a higher levelof abstraction to discover areas of interest and knowledge. Based uponresponse to stimuli, the administrator (the dealer) selects another deckto ascertain familiarity with increased detail to further refine thedepth and breadth of familiarity with related topics. As discussedabove, the dealer can be a user or automated.

In another embodiment, an EEG system is disclosed, comprising: sensors,amplifiers, analog filters, A/D converters, digital filters, noiserejection components, and signal extraction processing components; astimulus presentation system capable of reproducing images, video, andsounds, synchronized in time with the sensors, implemented with one ormore decks of analog or digital stimulus files which are reproducedserially for reception by human senses and illicit psychophysiologicresponse recorded by the EEG system, wherein the stimulus decks arepresented systematically in order of broad divisions of information toprogressively greater detail and specificity to discover vocationalknowledge, interest and experience, configured such that indication ofrecognition of stimuli in one deck guides selection of subsequent decksto obtain additional detail on vocational knowledge, interest andexperience; and an automated analysis system that extracts andcharacterizes brainwave signals indicative of recognition of reproducedby the stimuli presentation system.

In this embodiment, additional senses may be reproduced and presented tothe test subject, including taste, smell and touch; the stimulus deckmay be created by hand or by machine; the stimulus deck may be createdbefore presentation or in real-time based on indications of recognitionin previous stimulus decks; the dealing the deck may be controlled by adealer or be automated; dealer control (in person or automation) may beproximal with the test subject or from a remote location; and/or thesystem may assess the level of test-subject cooperation and adjustrecognition scores accordingly.

Lastly, in a final embodiment, this invention teaches a system capableof deducing the vocation of an individual without prior knowledge andwithout engaging a person in verbal questions. The system is composed ofan EEG subsystem, a stimulus presentation subsystem, a system of storedrecords of stimuli and an automated data processing subsystem. The EEGsubsystem is composed of multiple channels of sensors, amplifiers,analog filters and analog-to-digital converters. The stimuluspresentation subsystem is capable of reproducing multiple records ofimages, video, or sounds stored in analog or digital files that formdecks of stimulus data. The stimulus presentation system is synchronizedwith the EEG system so that the time of presentation and identity of thestimulus record are associated with the EEG data. Stimulus files in adeck are reproduced serially at a rapid pace for exposure to humansenses which result in a psychophysiologic response sensed and recordedby the EEG subsystem. The automated data processing system extracts andcharacterizes brainwave signals from EEG data to indicate the level ofrecognition of each stimulus. Multiple stimulus decks are presented in asystematic approach starting with broad divisions of information toprogressively greater detail and specificity to discover vocationalknowledge, interest, and experience of the person being monitored.Indication of recognition of stimuli in one deck guides automatedselection of subsequent decks to obtain additional detail on vocationalknowledge, interest, and experience.

All or part of the systems and methods described herein may beimplemented as a computer program product that is a non-transitorycomputer-readable storage medium encoded with computer code that isexecutable by a processor. All or part of the systems and methodsdescribed in this application may be implemented as an apparatus,method, or electronic system that may include one or more processors andstorage devices that store executable computer program code to implementthe stated functions.

The details of one or more embodiments of the subject matter of thisapplication are set forth in the drawings and descriptions contained inthis application. Other features, aspects, and advantages of the subjectmatter will become apparent from the description above, drawings, andclaims.

The subject matter of this specification functions in a variety ofcomponent combinations and contemplates all those types of components aperson of ordinary skill in the art would find suitable for functionsperformed. The figures describe specific components in specificembodiments. However the range of the types of components mentioned inthe description of the figures may be applied to other embodiments aswell.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. The terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The subject matter of this specification is described above withreference to system diagrams, flow diagrams, and screen mockups ofsystems, methods, and computer program products. Each block orcombinations of blocks in the diagrams can be implemented by computerprogram code and may represent a module, segment, or portion of code.Program code may be written in any combination of one or moreprogramming languages, including object oriented programming languagessuch as the JAVA®, SMALLTALK®, C++, C #, OBJECTIVE-C® programminglanguages and conventional procedural programming languages, such as the“C” programming language.

It should be noted that, in some alternative implementations, thefunctions noted in the blocks may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block or combination of blocksin the diagrams can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts.

Computer program code may be provided to a processor or multipleprocessors of a computer to produce a particular machine, such that theprogram code, which executes via the processor, create means forimplementing the functions specified in the system diagrams, flowdiagrams, and screen mockups.

The subject matter of this specification may be implemented on one ormore physical machines. Each physical machine may be a computercomprising one or more processors and one or more storage devices;however a single processor and a single storage device are sufficient. Aperson of ordinary skill in the art will recognize the variety of typesof computers suitable for the functions described, including desktops,laptops, handset devices, smartphones, tablets, servers, or accessoriesincorporating computers such as watches, glasses, or wearablecomputerized shoes or textiles. A non-exhaustive list of specificexamples of computers includes the following: Dell ALIENWARE™ desktops,Lenovo THINKPAD® laptops, SAMSUNG™ handsets, Google ANDROID™smartphones, Apple IPAD® tablets, IBM BLADECENTER® blade servers,PEBBLE™ wearable computer watches, Google GLASS™ wearable computerglasses, or any other device having one or more processors and one ormore storage devices, and capable of functioning as described in thisapplication.

A processor may be any device that accepts data as input, processes itaccording to instructions stored in a storage component, and providesresults as output. A person of ordinary skill in the art will recognizedthe variety of types of processors suitable for the functions disclosed,including general purpose processing units and special purposeprocessing units. A non-exhaustive list of specific examples ofprocessors includes the following: Qualcomm SNAPDRAGON™ processors;Nvidia TEGRA® 4 processors; Intel CORE™ i3, i5, and i7 processors; TEXASINSTRUMENTS™ OMAP4430; ARM® Cortex-M3; and AMD OPTERON™ 6300, 4300, and3300 Series processors. Each computer may have a single processor ormultiple processors operatively connected together (e.g. in the“cloud”).

A storage device is any type of non-transitory computer readable storagemedium. A person of ordinary skill in the art will recognized thevariety of types of storage devices suitable for the functionsdisclosed, including any electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system or device, so long as it does notreduce to a transitory or propagating signal. A non-exhaustive list ofspecific examples of storage devices includes the following: portablecomputer diskettes, hard disks, random access memory, read-only memory,erasable programmable read-only memory, flash memory, optical fibers,portable compact disc read-only memory, optical storage devices, andmagnetic storage devices. Each computer may have a single storage deviceor multiple storage devices operatively connected together (e.g. in the“cloud”).

This disclosure may be implemented on one or more computers running oneor more instances of a virtual machine. A virtual machine is a softwareimplementation of a computer that executes programs like a physicalmachine. Thus a single physical machine may function conventionally as aphysical computer, while also implementing a virtual machine that canperform the same processes as the physical computer. Multiple instancesof a virtual machine may run on one computer or across multiplecomputers. A person of ordinary skill in the art will recognize thevariety of types of virtual machines suitable for the functionsdisclosed, including system level virtual machines, process levelvirtual machines, fictive computers, and distributed computers. Anon-exhaustive list of specific examples of virtual machines includesthe following: VMWARE® virtual machines and Oracle VM VIRTUALBOX™virtual machines.

Embodiments of this disclosure that employ virtual machines may containa hypervisor, which is also known as a virtual machine monitor. Ahypervisor is a piece of computer software that creates, runs, andmanages virtual machines. More than one virtual machine may be run by asingle hypervisor. The hypervisor controls the utilization of one ormore processors by one or more virtual machines and the utilization ofone or more storage devices by one or more virtual machines. A person ofordinary skill in the art will recognized the variety of types ofhypervisors suitable for the functions disclosed, including type one or“native” hypervisors, and type two or “hosted” hypervisors. Anon-exhaustive list of specific examples of hypervisors includes: OracleVMWARE® Server for SPARC, Oracle VM SERVER™ for x86, Citrix XENSERVER™,and VMWARE® ESX/ESXi.

For the purposes of this application, the term “computing component”means a computer, a virtual machine, or multiple computers or virtualmachines functioning as a single component. The term “computer” islimited to physical machines. Generally a computer functions as acomputing component by implementing an operating system through whichprogram code, which implements the methods of this system, is executed.Generally, when a virtual machine functions as a computing component, acomputer implements a hypervisor which implements a separate operatingsystem, through which the program code is executed.

As referenced above, a single computer may implement multiple computingcomponents, wherein the computer itself functions as a computingcomponent and concurrently implements one or more instances of a virtualmachine. Each virtual machine functions as a separate computingcomponent. Similarly, a plurality of computing components may be made upof separate computers, none of which implement a virtual machine, or aplurality of computing components may be implemented on a singlecomputer wherein only the virtual machines function as computingcomponents. Additional combinations are contemplated as well, such aswhere a computing component is implemented across multiple computers.For example, a hypervisor of a virtual machine may manage the processorsand storage devices of three computers to implement a virtual machinethat functions as a single computing component. A person of ordinaryskill in the art will recognize the range of combinations of computersand virtual machines that are suitable for the functions disclosed.

Computing components may be operatively connected to one another orother devices, such as by a communications network. One skilled in theart will recognize the appropriate media over which multiple computingcomponents may be operatively connected to each other in a mannersuitable for the functions disclosed, including as a communicationsnetwork that allows the computing components to exchange data such thata process in one computing component is able to exchange informationwith a process in another computing component. A non-exhaustive list ofspecific examples of transmission media includes: serial or parallel bussystems, wireless, wireline, twisted pair, coaxial cable, optical fibercable, radio frequency, microwave transmission, or any otherelectromagnetic transmission media.

The above components are described in greater detail with reference tothe figures. The descriptions set forth the various processes,relationships, and physical components of various embodiments of thesubject matter of this disclosure.

The invention claimed is:
 1. A method for obtaining information of atleast one of knowledge, interest and experience of a subject for aspecific category of knowledge, the method comprising: receiving, by aprocessor, a first signal, wherein the first signal represents a firstbrainwave response of the subject to a first stimulus associated withfirst information; determining, by the processor, a first correlationbetween the first brainwave response and a knowledge category, whereinthe knowledge category is a first node of a hierarchical tree structurefor categories of knowledge; receiving, by the processor and after adetermination of the first correlation, a second signal, wherein thesecond signal represents a second brainwave response of the subject to asecond stimulus associated with second information in greater detail andspecificity than the first information; determining, by the processor, asecond correlation between the second brainwave response and asub-category of the knowledge category, wherein the sub-category is asecond node representing the specific category of knowledge of thehierarchical tree structure for the categories of knowledge; andobtaining information of at least one of knowledge, interest andexperience of the subject based on the determined second correlationbetween the second brainwave response and the sub-category of theknowledge category.
 2. The method of claim 1, wherein at least one ofthe first signal or the second signal comprises an electroencephalogram.3. The method of claim 1, wherein at least one of: the receiving thefirst signal comprises receiving the first signal from a sensor; or thereceiving the second signal comprises receiving the second signal fromthe sensor.
 4. The method of claim 3, where in the sensor comprises anelectrode.
 5. The method of claim 1, wherein at least one of: thedetermining the first correlation comprises determining a presence of ap-300 signal at a point in the first signal that has a correspondencewith a time at which the subject was exposed to the first stimulus; orthe determining the second correlation comprises determining a presenceof the p-300 signal at a point in the second signal that has acorrespondence with to a time at which the subject was exposed to thesecond stimulus.
 6. The method of claim 5, wherein at least one of: thecorrespondence with the time at which the subject was exposed to thefirst stimulus comprises the point in the first signal being about 300milliseconds after the time at which the subject was exposed to thefirst stimulus; or the correspondence with the time at which the subjectwas exposed to the second stimulus comprises the point in the secondsignal being about 300 milliseconds after the time at which the subjectwas exposed to the second stimulus.
 7. The method of claim 1, wherein atleast one of: the determining the first correlation comprises processingthe first signal through a classifier; or the determining the secondcorrelation comprises processing the second signal through theclassifier.
 8. The method of claim 7, further comprising training, bythe processor, the classifier with a signal produced in response toexposing the subject to at least one of a stimulus known to be familiarto the subject or a stimulus known to be recognized by the subject. 9.The method of claim 1, further comprising determining, by the processor,a correlation between at least one of the first brainwave response orthe second brainwave response and an indication of at least one of: aninattentiveness of the subject to at least one of the first stimulus orthe second stimulus; or a muscle movement by the subject at a time of atleast one of the first stimulus or the second stimulus.
 10. The methodof claim 1, wherein at least one of the first stimulus or the secondstimulus excludes a question presented to the subject.
 11. The method ofclaim 1, wherein at least one of the first stimulus or the secondstimulus is included in a sequence of stimuli.
 12. The method of claim11, wherein the sequence of stimuli is presented to the subject at arate of between four and twelve stimuli per second.
 13. The method ofclaim 11, further comprising repeating, by the processor and initerations, at least one of the receiving the first signal or thereceiving the second signal, wherein at least one of: a position of thefirst stimulus in a first iteration is different from a position of thefirst stimulus in a second iteration; or a position of the secondstimulus in the first iteration is different from a position of thesecond stimulus in the second iteration.
 14. The method of claim 1,further comprising selecting, by the processor and after thedetermination of the first correlation, the second stimulus.
 15. Anon-transitory computer-readable medium storing computer code forobtaining information of at least one of knowledge, interest andexperience of a subject for a specific category of knowledge, thecomputer code including instructions to cause the processor to: receivea first signal, wherein the first signal represents a first brainwaveresponse of the subject to a first stimulus associated with firstinformation; determine a first correlation between the first brainwaveresponse and a knowledge category, wherein the knowledge category is afirst node of a hierarchical tree structure for categories of knowledge;receive, after a determination of the first correlation, a secondsignal, wherein the second signal represents a second brainwave responseof the subject to a second stimulus associated with second informationin greater detail and specificity than the first information; determinea second correlation between the second brainwave response and asub-category of the knowledge category, wherein the sub-category is asecond node representing the specific category of knowledge of thehierarchical tree structure for the categories of knowledge; and obtaininformation of at least one of knowledge, interest and experience of thesubject based on the determined second correlation between the secondbrainwave response and the sub-category of the knowledge category.
 16. Asystem for obtaining information of at least one of knowledge, interestand experience of a subject for a specific category of knowledge, thesystem comprising: a memory configured to store a first signal and asecond signal; and a processor configured to: receive the first signal,wherein the first signal represents a first brainwave response of thesubject to a first stimulus associated with first information; determinea first correlation between the first brainwave response and a knowledgecategory, wherein the knowledge category is a first node of ahierarchical tree structure for categories of knowledge; receive, aftera determination of the first correlation, the second signal, wherein thesecond signal represents a second brainwave response of the subject to asecond stimulus associated with second information in greater detail andspecificity than the first information; determine a second correlationbetween the second brainwave response and a subcategory of the knowledgecategory, wherein the sub-category is a second node representing thespecific category of knowledge of the hierarchical tree structure forthe categories of knowledge; and obtain information of at least one ofknowledge, interest and experience of the subject based on thedetermined second correlation between the second brainwave response andthe sub-category of the knowledge category.
 17. The system of claim 16,wherein at least one of: the processor is configured to determine thefirst correlation by determining a presence of a p-300 signal at a pointin the first signal that has a correspondence with a time at which thesubject was exposed to the first stimulus; or the processor isconfigured to determine the second correlation by determining a presenceof the p-300 signal at a point in the second signal that has acorrespondence with to a time at which the subject was exposed to thesecond stimulus.
 18. The system of claim 17, wherein at least one of:the correspondence with the time at which the subject was exposed to thefirst stimulus comprises the point in the first signal being about 300milliseconds after the time at which the subject was exposed to thefirst stimulus; or the correspondence with the time at which the subjectwas exposed to the second stimulus comprises the point in the secondsignal being about 300 milliseconds after the time at which the subjectwas exposed to the second stimulus.
 19. The system of claim 16, whereinat least one of: the processor is configured to determine the firstcorrelation by processing the first signal through a classifier; or theprocessor is configured to determine the second correlation byprocessing the second signal through the classifier.
 20. The system ofclaim 19, wherein the processor is further configured to train theclassifier with a signal produced in response to exposing the subject toat least one of a stimulus known to be familiar to the subject or astimulus known to be recognized by the subject.
 21. The system of claim16, wherein the processor is further configured to determine acorrelation between at least one of the first brainwave response or thesecond brainwave response and an indication of at least one of: aninattentiveness of the subject to at least one of the first stimulus orthe second stimulus; or a muscle movement by the subject at a time of atleast one of the first stimulus or the second stimulus.
 22. The systemof claim 16, wherein the processor is further configured to select,after the determination of the first correlation, the second stimulus.